mlsanityData to 14 Oct 2026

Dynamic Contextual Attention Network: Transforming Spatial Representations into Adaptive Insights for Endoscopic Polyp Diagnosis

arXiv 2504.20306 ↗First posted 28 Apr 2025cs.CVcited by 2

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Abstract (arXiv)

Colorectal polyps are key indicators for early detection of colorectal cancer. However, traditional endoscopic imaging often struggles with accurate polyp localization and lacks comprehensive contextual awareness, which can limit the explainability of diagnoses. To address these issues, we propose the Dynamic Contextual Attention Network (DCAN). This novel approach transforms spatial representations into adaptive contextual insights, using an attention mechanism that enhances focus on critical polyp regions without explicit localization modules. By integrating contextual awareness into the classification process, DCAN improves decision interpretability and overall diagnostic performance. This advancement in imaging could lead to more reliable colorectal cancer detection, enabling better patient outcomes.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 28 Apr 2025.

Builds on

9 of 20 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Squeeze-and-Excitation Networkscited by 4,789
  3. 2017Non-local Neural Networkscited by 2,293
  4. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  5. 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 272
  6. 2020Rotate to Attend: Convolutional Triplet Attention Modulecited by 75
  7. 2020Global Context Networkscited by 35
  8. 2024Guided Context Gating: Learning to leverage salient lesions in retinal fundus imagescited by 7
  9. 2020A Deep Convolutional Neural Network for the Detection of Polyps in Colonoscopy Imagescited by 3

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Citing papers per year: 2026: 2.

2 papers.